Risk-return Profile Research Articles

Examining the Performance of Select Multi-Cap Funds in India

Mutual Fund offers a vast platform to all investors for active portfolio diversification through professional management of savings and investment in numerous securities with heterogenic risk-return profiles. The Assets under Management of Indian Mutual Fund industry has increased drastically from only Rs.25 Crores in 1964 to Rs.27.26 lakh Crores in December 2019. Multi-Cap Funds invest in equity and equity-related instruments, which is 65% of the total assets; that invest in Large-Cap, Mid-Cap and Small-Cap stocks. This study focuses on how the Open-ended Multi-Cap Funds has performed during the last seven years (2013-2019); based on various parameters; selecting ten schemes having net assets above Rs.5,000 Crores. It has been observed that Kotak Standard Multi-cap Fund and Axis Focused 25 Fund have outperformed the benchmark (S&P BSE 500) for return (CAGR) and risk-adjusted return respectively. Franklin India Equity Fund has outperformed the benchmark in terms of total risk. Further, Franklin India Focused Equity Fund, Nippon India Multi-Cap Fund and HDFC Equity Fund have remained aggressive during the entire period of study.

A Performance Evaluation of Ethical Mutual Funds: Evidence from India

Since the last decade, ethical investment has gained significant development in the Indian financial market. With the increasing focus on social responsibility and the importance of ethical funds, investors pay more attention to the activities of these funds and their impact on society. Whether ethical fund investment affects portfolios’ return performance is still questionable. To address this question, this paper investigated the performance of various ethical mutual fund schemes and compared them with benchmark index (NIFTY 500 Shariah Index) using data from 2014-15 to 2019-20. The performance of the sample schemes was measured based on the risk-return profile and Sharpe’s ratio, Treynor’s ratio and Jensen’s alpha measures. The results indicated that nine out of ten ethical schemes outperformed the benchmark index during the study period. This implies that ethical funds generated positive returns with lower risk. As a policy implication, investment funds and financial institutions should develop fund schemes and stocks that incorporate ethical considerations in an investment.

How do banks invest in fintechs? Evidence from advanced economies

Using data on a sample of 803 investment rounds in fintech companies (FTCs) held between 2008 and 2018 at the global level, where each company received investment from at least one financial institution, we investigate how banks react to digital transformation through the direct investment channel. Each round is treated as an independent event; hence, we examine 1,078 bank-FTC observations. Employing OLS regressions, we explore the determinants of deal size, both in absolute (i.e., monetary flows) and relative terms (i.e., the proportion of the total funding that the target has cumulatively received). Conversely, with probit analysis, we investigate the determinants of banks’ choices to invest in “fin-native” vs. “tech-native” FTCs and those of their decisions to use equity (i.e., capital injections) vs. debt (i.e., credit lines). We find that investments are enhanced by an FTC’s specialisation in financial rather than technological services, by the presence of other investors, and — to some extent — when the target is in a later stage of development and the bank is large. Additionally, the nature of a target depends largely (but not exclusively) on the corresponding bank’s risk-return profile, as fin-native companies and debt financing are perceived as safer than tech-native companies and equity financing, respectively. Finally, we find that a limited but non-negligible role is played by the regulatory framework, which mainly affects the choice of financing tool by prompting banks to choose equity — i.e., a bolder risk-return profile — when they face harsher competition from unlicensed lenders.

The Effect of Ship Owner-Ship Operator Separation on the Market Risk Split

Purpose - There are three different business models in shipping: owner-operators, operators, and owners. This paper analyzes the effect of separating owner-operators into tonnage providers and operators, and considers this effect from the perspective of risks and returns. Design/Methodology/Approach - A historical simulation was performed to measure realized operating profits and the volatility of each type of player. Time series data from 1992 was adopted from Clarkson Research and Drewry Maritime Research to simulate revenues, capital costs, and operating costs for 229 investment cases. A bootstrap sampling enhances the robustness of the results. Findings - The results show that the operating profits of owner-operators are divided into those of two separate players. The same, however, cannot be said for market risk. Against general expectations, operator market risk was not significantly reduced by separation; in the case of Panamax, it even increased. This can be explained by a negative correlation between spot rates and actually-applied time charter rates. Research Implication - Most papers on market risk have so far focused on the measurement and forecast of return volatility. This paper, on the other hand, is a novel attempt to analyze the effect of business model separation on market risk. The public-sector owner model is widely discussed in Korean shipping circles, and this paper is expected to provide meaningful insight to policymakers. Additionally, the risk-return profile of the pure owner model provides a good basis to develop the model for the balanced growth of the Korean shipping industry.

Portfolio Construction and New Energy Infrastructure Investing

In light of the increasingly severe consequences of climate change, an increasing number of institutional investors want their investments to replicate their ethical values. This article identifies the decarbonization of global power generation as an opportunity for those investors requiring sustainable impact while enhancing the risk-adjusted returns for, and future-proofing of, their portfolios. After analyzing the worsening state of financial markets, with high equity valuations and record-low bond yields, the authors demonstrate the added benefit of economic resilience of sustainable infrastructure assets. A move into sustainable infrastructure is bolstered by global political tailwinds earmarking fiscal stimulus to upgrade existing and new green infrastructure, as well as regulators increasingly enforcing disclosure of progress. Finally, by delving deeper into portfolio construction, the authors argue for the benefits of diversifying into the adjacent renewable-energy technologies needed to sustain the forthcoming renewable-power networks to optimize the risk–return profile of portfolios.

Chaos based portfolio selection: A nonlinear dynamics approach

Time series forecasting is of fundamental importance for financial market prediction and, consequently, for portfolio allocation strategies. However, non-stationarity and non-linearity of most financial time series often make these tasks difficult to perform. In this paper, we propose a methodology based on chaos and dynamical systems theory for non-linear time series forecasting and investment strategy development, which is able to correctly make predictions at long time horizons. We construct Constant Chaoticity Portfolios (CCP) and evaluate their performances on the survival components of the STOXX Europe 50 index and the Hang-Seng index. Results show that the CCP overwhelms several competing alternatives, both in terms of net profits and risk-return profiles. Our findings are confirmed by a sensitivity analysis on the parameters of the underlying model and over different choices of forecast horizons.

Impact of gender diversity in boardroom on risk-return profile of Indian corporates

PurposeThe existing literature point that the presence of women directors in a firm reduces its risk. However, the relation between boardroom gender diversity and a firm’s return is widely disputed leading to no concrete answer. Some studies mention that women directors have a positive impact on firm performance, whereas, on the other hand, some findings suggest that women directors reduce financial performance. This paper aims to study the relationship of firm risk and return with boardroom gender diversity and the net impact on firm performance in the Indian context. This study uses not only traditional measures of risk and return but also the third measure of risk-adjusted returns to postulate its findings.Design/methodology/approachBased upon the data of the top 100 of the Bombay Stock Exchange-500 firms for the period FY 2009–2010 to FY 2018–2019, this study applied fixed effect panel regression and random effects Tobit regression to examine the effect of board gender diversity on firm performance.FindingsThe study concludes that firms with women directors on board have lower risk and lower returns. It also results in a higher risk-adjusted return, creating a positive impact on a firm’s performance.Originality/valueThe paper contributes to the existing literature on corporate governance by considering return, risk and risk-adjusted returns in single research to have a holistic measure of firm performance. It provides empirical evidence from one of the largest emerging economies, India where the female director and independent female director have been introduced recently.

Interest rate structured products: can they improve the risk–return profile?

In this paper, we investigate the contribution of interest rate structured bonds to portfolios of risk-averse retail investors. We conduct our analysis by simulating the term structure according to a multifactor no-arbitrage interest rate model and comparing the performance of a portfolio consisting of basic products (zero-coupon bonds, coupon bonds and floating rate notes) with a portfolio containing more sophisticated exotic products (like constant maturity swaps, collars, spread and volatility notes). Our analysis, performed under different market environments, as well as volatility and correlation levels, takes into account the combined effects of risk premiums required by investors and fees that they have to pay. Our results show that capital protected interest rate structured products allow investors to improve risk–return trade-off if no fees are considered. With fees, our simulations show that structured products add value to the basic portfolio in a very limited number of cases. We believe our paper contributes to understanding the role of structured products in investors portfolios also in light of the current regulatory debate on the use of complex financial products by retail investors.

Deep Neural Network and Time Series Approach for Finance Systems

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

Do retail investors bite off more than they can chew? A close look at their return objectives

Using self-reported retail investor information from a risk-return profile survey, we investigate the determinants of individual return objectives as well as the capacity of investors to achieve them. Controlling for a large set of covariates, we provide empirical evidence that return objectives are related to subjective individual characteristics (such as financial literacy and risk tolerance), some sociodemographic variables (age and education), and recent past trading intensity. Retail investors with higher return objectives perform better than their counterparts who want to avoid any risk of capital loss. The capacity to achieve the return objective, however, decreases as the level of the objective increases.

Low volatility and ESG investing combined: Invesco’s holistic approach

The low volatility factor in conjunction with the style factors Quality, Value and Momentum, has empirically proven to be able to moderate market risks and improve a portfolio’s overall risk-return profile. By integrating ESG into such a factor portfolio, future risks may be mitigated. We present a proprietary approach to managing ESG risks that can maximize sensitivities to the desired multi-factor characteristics, and we calculate Climate VaR under different global warming scenarios.

On the hedging benefits of REITs: The role of risk aversion and market states

We propose a dynamic, forward-looking hedging strategy to manage stock market risks via positions in REITs, conditional on the level of risk aversion. Our findings show that REITs do not only offer significant risk reduction for passive portfolios, but also offer much improved risk-adjusted returns with the greatest benefits observed for Australia, Canada and the U.S. Overall, our findings suggest that time-varying risk aversion can be utilized to (i) establish effective hedges against stock market risks via positions in REITS, and (ii) improve the risk-return profile of passive portfolios.

What Portfolio in Europe Makes Sense?

This article focuses on European bond and equity portfolios, and specifically the relevance of balanced portfolios. European equity and bond markets have undergone tremendous changes since the launch of the European Monetary Union (EMU) in 2000. Describing the risk–return profile of these markets provides valuable information about past structure and historical returns, and shifts the focus onto future expectations for institutional portfolios. The great volatility and low returns involved in European equity markets are a possible explanation for why investors prefer bonds that show Sharpe ratios higher than one. Taking into account five-year realized inflation, as well as strong bond market performance in the past 20 years with yields in the negative territory, the evidence indicates that the window for expected returns on balanced portfolios with large bond exposure is firmly shut. There are two challenging issues for European portfolio allocation. The first reflects the future of bond exposure in a balanced mandate. The second involves meaningful allocation to equity markets in a balanced portfolio. <b>TOPICS:</b>Security analysis and valuation, fixed income and structured finance, developed markets, portfolio construction <b>Key Findings</b> ▪ European bond markets have performed excellently in the last 20 years because of investor demand, capital inflows, and growth. The European bond market dominates in institutional portfolio allocations, and equity markets have been less attractive, given their risk–return characteristics. ▪ In a negative yield environment and where real equity returns are expected to be positive, it is questionable whether balanced allocation is capable of generating the desired financial outcomes. Foreign exchange, emerging market bonds, and global equities might provide desired returns for investors. ▪ Two challenging issues are involved in European portfolio allocation. The first reflects the future of bond exposure in a balanced mandate. The second involves meaningful allocation to equity markets in a balanced portfolio. Diversification has not disappeared, but finding expected returns might be challenging.

Interdependence between Green Financial Instruments and Major Conventional Assets: A Wavelet-Based Network Analysis

This paper examines the interdependence between green financial instruments, represented by green bonds and green stocks, and a set of major conventional assets, such as Treasury, investment-grade and high-yield corporate bonds, general stocks, crude oil, and gold. To that end, a novel wavelet-based network approach that allows for assessing the degree of interconnection between green financial products and traditional asset classes across different investment horizons is applied. The empirical results show that green bonds are tightly linked to Treasury and investment-grade corporate bonds, while green stocks are strongly tied to general stocks, regardless of the specific time period and investment horizon considered. However, despite their common climate-friendly nature, there is no a remarkable association between green bonds and green stocks. This means that these green investments constitute basically two independent asset classes, with a distinct risk-return profile and aimed at a different type of investor. Furthermore, green financial products have a weak connection with high-yield corporate bonds and crude oil. These findings can have important implications for investors and policy makers in terms of investment decision, hedging strategies, and sustainability and energy policies.

A Cryptocurrency Risk–Return Analysis for Bull and Bear Regimes

In this article, the authors develop a new analytical lens through which to examine the risk–return profiles of bitcoin, litecoin, ripple, and ethereum. Their focus is to understand better the price behavior of individual cryptocurrencies and their influence on one another. To achieve this, they segment each cryptocurrency’s time series of returns into disparate bull and bear regimes. They then examine the nature and extent of overlap between these regimes and whether they change over time. They also collect and plot several indicative distributed-denial-of-service attacks against the time series to investigate their possible impact on regime change episodes. Their findings shed light on previously unexplored systemic risk indicators within the cryptomarket as a whole and on the relationship between specific cryptocurrency pairs. These findings enhance the risk management toolkit for investors by revealing potential price behavior contagion patterns between cryptocurrencies pertinent to blended portfolio management. Furthermore, the authors’ approach serves as a blueprint for additional research into regime-type overlap within the cryptomarket. TOPICS:Real assets/alternative investments/private equity, exchanges/markets/clearinghouses, financial crises and financial market history Key Findings ▪ Periods of overlapping regimes increase over time. The increase indicates a rise in cryptomarket systemic risk and an associated reduction in the diversification value of a blended portfolio of cryptocurrencies. ▪ Bitcoin exhibits the most favorable risk measures across both bull and bear regimes, including the lowest proclivity for extreme events during bear regimes. In contrast, ripple displays the overall riskiest profile across both regime types. ▪ Bitcoin’s regime type has the most meaningful impact on the risk–return profile of other cryptocurrencies, namely, litecoin and ripple. However, this relationship does not hold in reverse, a likely consequence of bitcoin’s market dominance and relative maturity.

Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies

Cryptocurrencies (CCs) have risen rapidly in market capitalization over the past years. Despite striking volatility, their high average returns and low correlations have established CCs as alternative investment assets for portfolio and risk management. We investigate the benefits of adding CCs to well-diversified portfolios of conventional financial assets for different types of investors, including risk-averse, return-maximizing and diversification-seeking investors who may trade at different frequencies, namely, daily, weekly or monthly. We calculate out-of-sample performance and diversification benefits for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. Our results demonstrate that CCs can improve the risk-return profile of portfolios, but their benefit depends on investor objectives. In particular, diversification strategies (maximizing the portfolio diversification index or equating risk contributions) draw appreciably on CCs and show, in line with spanning tests, CCs to be non-redundant extensions of the investment universe. However, when we introduce liquidity constraints via the LIBRO method to account for illiquidity of many CCs, out-of-sample performance drops considerably, while the diversification benefits persist. We conclude that the utility of CC investments strongly depends on investor characteristics.

Can an equity structure dominate the risk-return profile of corporate bonds?

In a very low interest rate and tight credit spread environment, institutional investors look for alternatives to enhance their fixed income portfolios whilst maintaining their risk profile. Some have devised hedge funds as a solution to improve return at comparable risk levels. More recently, some authors show that appropriate optional strategies could offer better risk-return profiles than hedge funds. This paper proposes a synthetic equity derivative structure that mimics the cash flow behaviour of a corporate bond portfolio. Both alternatives are empirically compared, balancing their yield with their level of risk, and integrating the probability of default. In the case of the high-rating class, our results show that the derivative structure offers a better “spread-default” profile than that of corporate bonds. This may have interesting implications for insurers and pension funds that seek to invest a substantial fraction of their portfolio in high-grade fixed income assets and is consistent with the focus of liability-driven investors on yearly cash flow requirements.

Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis

Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments.

Debt for Climate: Green Bonds and Other Instruments

This chapter, prepared for the Edward Elgar Research Handbook on Climate Finance and Investment Law (2020, Michael Mehling and Harro van Asselt (eds.)), examines the rise of green bonds, climate bonds, and other green financial instruments. Although climate finance has enjoyed positive momentum in recent years, this momentum is at risk—with the possibility of reversal—if climate markets fail to provide competitive risk-adjusted returns. For climate finance to compete effectively, governments, issuers, and investors must resolve a set of interrelated problems. First, green investments must compete with traditional “brown” investments, some of which enjoy significant subsidies. Even when not competing against subsidized industries and investments, green investments may still lack an advantageous risk-return profile and may suffer lower demand compared to standard investments. Second, green investments must create sufficient trust in their “greenness” so as to avoid accusations of greenwashing that would not only jeopardize a given issuer’s offerings, but would also erode trust in green markets generally. This chapter describes public and private efforts to develop a robust deal infrastructure for climate finance, including mechanisms like distributed ledgers and other financial technologies that are designed to make green investments more competitive with brown investments, while also increasing trust and reducing transaction costs.

Alternative investments in the Fintech era: The risk and return of Non-Fungible Token (NFT)

We utilize one of the earliest and largest NFT collections to investigate the pricing and the risk-return profile of NFTs. In general, we find that NFTs have higher returns than traditional financial assets. Yet, investing in NFTs comes along with extremely high volatility. The average monthly returns on NFTs range from 6.10% to 44.11%. But their standard deviations fluctuate between 44.35% and 74.57%, leading to a Sharpe ratio comparable to the NASDAQ index. NFT prices surge when there is a drastic increase in demand for alternative investments and a search for yield, especially in a low interest rate environment. We also find that the pricing of NFT largely depends on a token’s scarceness and an investor’s aesthetic preference. Hence, conventional asset-pricing models are unlikely to explain NFT returns. Overall, we provide the first comprehensive analysis that NFTs serve as a novel investment vessel in this Fintech era.